paentderivedinducedpluripotentstem ... lo… · q136 2.3972 1.83e-12 web-based image annotation...
TRANSCRIPT
Pa#ent-‐derived induced pluripotent stem cells as a therapeu#cs discovery pla6orm:
challenges and opportuni#es
Steven Finkbeiner, MD, PhD Professor, UCSF & Gladstone Director Taube/Koret Center
CIRM Webinar
The Enormous Therapeutic Challenge of Neurodegenerative Disease!
> 20% over age 65
Change in Number of Deaths2000–2008"
Neurodegenerative Diseases – Challenges and Opportunities
The Challenge of Drug Development for Neurological Disease
© 2007 Harvard NeuroDiscovery Center
FDA-approved Drugs 1996-2007
‘04 -‐ ‘07
‘00 -‐ ‘03
‘96 -‐ ‘99
0
10
20
30
40
50
60
Infectious Disease CardiovascularDisease
Cancer Neurodegeneration
’08 - ‘11
© 2011 Harvard NeuroDiscovery Center. Courtesy Adrian Ivinson. Data from Thompson Centerwatch
No effective disease modifying therapy exists for any major neurodegenerative disease!
Neurodegenerative Diseases – Challenges and Opportunities
Murine Models of Human Disease
Genetic similarities are striking…
…but to a drug, mice and people may be very different
Neurodegenerative Diseases – Challenges and Opportunities
Patient fibroblasts
Induced pluripotent stem cells Human
neurons
Developing Disease Models Based on Patient-Derived Induced Pluripotent Stem Cells
SMI32 HB9 DAPI
Characterizing Human iPSCs Differentiated Toward Brain Cells
Motor Neurons
S100ß/GFAP
Astrocytes
Oligodendrocytes
Phospholipid protein DAPI Nestin
40 % of the cells are Dopaminergic neurons (TH+) FluorMyelin Red
Cell Type Differentiation Efficiency Forebrain neurons 3 weeks ~90% Striatal neurons 8 weeks ~1-5% Dopaminergic neurons 4-8 weeks ~50-70%
Motor neurons 6 weeks ~40-50% Astrocytes 12-24 weeks ~25% Oligodendrocytes 4-18 weeks ~1-10% Microglia-like 6 weeks ~10-20%
Challenges to iPSCs as a Discovery/Analytical Platform
Heterogeneity remains a challenge • Line-specific variation (possible importance of
reprogramming techniques; donor heterogeneity) • Prep variation (differentiation) • Maturity (persistence of dividing cells) • Cell type
Disease Muta#on Number Of Pa#ents
Number Of iPSC Clones
HunCngton’s Disease 26-‐70 CAG expansion 10 28
>70 CAG expansion 4 13
Parkinson’s Disease LRRK2 G2019S 2 2
LRRK2 G2019S (at risk) 2 2
LRRK2 R1441C (at risk) 2 2
α-‐SYNUCLEIN triplicaCon 4 4
PARK2 1 1
Sporadic PD 1 1
ALS TDP43 3 7
ANGIOGENIN 1 1
SOD1 (Fast & Slow Progressing) 7 7
FUS 3 3
c9ORF72 expansion 7 7
Sporadic ALS (Fast & Slow Progressing) 6 6
FTD PROGRANULIN 1 3
Healthy Controls (relaCves and unrelated)
-‐-‐ 24 37
Total 78 124
Addressing Heterogeneity: Making Many iPSC Lines
ConfidenCal
• Isogenic controls
• Lineage reporter lines
Julia Kaye & Kelly Haston
Addressing Heterogeneity: Genome Editing
Addressing Heterogeneity: Scaling Differentiation
Self-‐Renewing iPS Cells
Progenitors
• Motor Neurons • Forebrain Neurons
• Astrocytes
Characterized and Banked iPSC-‐Neurons
Transfected iPSC-‐Neurons Imaged by
Automated microscopy
iPSC-‐Neurons Grown In Monolayer
Ashkan Javaherian
High Throughput Longitudinal Single Cell Analysis
3rd Generation Automated Microscope (Robo III)
7 day movie
#thebrainbot
Day 1 Day 2 Day 3 Day 7 Day 6 Day 5 Day 4
Well suited to deal with cell-specific stochastic phenomena - Reduced user bias - Reduced observer
bias - Powerfully exploits
cell-to-cell variability
Morphology & viability
Levels & aggregation of disease protein
Function #1 Function #2
Transcription
Assays for Physiologic and Pathophysiologic Phenotypes"
Mitochondria
Gene Transcription
Proteasome Function
Autophagy
AMPAR cycling
Spines
Google Collaboration II: Overview
Raw Images Neuro- Analysis Pipeline
Measurements of Tracked Brain Cells
Construct Risk of Death
P Value
Q17 0.8623 0.269
Q136 2.3972 1.83e-12
Web-based Image Annotation
Results
Machine Learning
Goal: Substantially More Powerful Image Analysis
• “Systems”cell biology
• Quantitative predictive model of fate
• Blueprint for rational intervention
Confidential – not in the public domain
Human iPSC-based Models of Neurodegenerative Disease ALS/ FTD
PNAS, 2012 Cell Stem Cell, 2012; PNAS, 2013; J. Neurosci, 2014; Nat. Chem. Biol., 2014, in press
LRRK2 G2019S
Huntington’s Disease
Parkinson’s Disease
Disease-relevant phenotypes: • Survival • Neurite length • Calcium signaling • Glial phenotypes • Electrophysiology changes • Gene profiling changes • Response to stress
Current Applications & Future Directions
• Mechanisms of disease (NIH, CIRM, Foundations)
• Genetics (NIH, Michael J Fox Foundation, Pharma) • Bottom up: RNAi screens • Top down: functional
validation of human genetics results
• Small molecule discovery (ALS-TDI, Pharma)
• Lead optimization (NIH, Pharma)
• Long-term toxicology, especially when mouse models won’t do (Pharma)
• More relevant/complex models
• Integrate AI / deep learning into phenotypic analysis (Google) to predict clinical results
Human neuromuscular junction
SciBX 3:5-8, 2010
iPSCs & Small Molecule Discovery and Lead Optimization: The Development of Small Molecule Autophagy Inducers
Application of Small Molecule Autophagy Inducers Mitigates TDP43 (A315T) Toxicity
TDP43 (A315T) in Rodent Neurons
Patient-derived human motor neurons (TDP43
M337V)
Patient-derived human astrocytes
(TDP43 M337V)
Barmada et al., Nat. Chemical Biology, 2014; Javaherian et al. unpublished
New 3rd Generation inducers: Novel structures with potencies < 5 nM, better side effect profile, and good predicted BBB penetration
CIRM ETA
Finkbeiner Lab! Mike Ando!Arpana Ardun!Montserrat Arrasate !Sami Barmada!Matthew Campioni!Maya Chandru !!Aaron Daub!Lisa Elia!Kelly Haston!Ashkan Javaherian!Julia Kaye!Ian Kratter!
Acknowledgments Eva LaDow!Julia Margulis!Amanda Mason!Jason Miller!Siddhartha Mitra!Gaia Skibinski!Tina Tran!Andrey Tsvetkov!Hengameh Zayed!Adam Ziemann!
HD iPSC Consortium!Jim Gusella !Marcy MacDonald!Chris Ross!Clive Svendsen!Leslie (again!)!Nick Allen!Paul Kemp!!Institute for Systems Biology!Jared Roach!David Galas!Lee Hood!
UCI!Joan Steffan!Leslie Thompson!